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Abstract
Data mining or knowledge discovery refers to the process of finding interesting
information in large repositories of data. The term data mining also refers to the step in
the knowledge discovery process in which special algorithms are employed in hopes of
identifying interesting patterns in the data. These interesting patterns are then analyzed
yielding knowledge. The desired outcome of data mining activities is to discover
knowledge that is not explicit in the data, and to put that knowledge to use.
Librarians involved in digital libraries are already benefiting from data mining
techniques as they explore ways to automatically classify information and explore new
approaches for subject clustering (MetaCombine Project). As the field grows, new
applications for libraries are likely to evolve and it will be important for library
administrators to have a basic understanding of the technology.
A wide variety of data mining techniques are also employed by industry and
government. Many of these activities pose threats to personal privacy. As professionals
ethically bound to ensure that individual privacy is safe-guarded, data mining activities
should be monitored and kept on every librarian’s radar.
This paper is written for information professionals who would like a better
understanding of knowledge discovery and data mining techniques. It explains the
historical development of this new discipline, explains specific data mining methods, and
concludes that future development should focus on developing tools and techniques that
yield useful knowledge without invading individual privacy.

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Introduction
Data mining is an ambiguous term that has been used to refer to the process of
finding interesting information in large repositories of data. More precisely, the term
refers to the application of special algorithms in a process built upon sound principles
from numerous disciplines including statistics, artificial intelligence, machine learning,
database science, and information retrieval (Han & Kamber, 2001).
Data mining algorithms are utilized in the process of pursuits variously called data
mining, knowledge mining, data driven discovery, and deductive learning (Dunham,
2003). Data mining techniques can be performed on a wide variety of data types
including databases, text, spatial data, temporal data, images, and other complex data
(Frawley, Piatetsky-Shapiro, & Matheus, 1991; Hearst, 1999; Roddick & Spiliopoulou,
1999; Zaïane, O.R., Han, J., Li, Z., & Hou, J, 1998).
Some areas of specialty have a name such as KDD (knowledge discovery in
databases), text mining and Web mining. Most of these specialties utilize the same basic
toolset and follow the same basic process and (hopefully) yield the same product – useful
knowledge that was not explicitly part of the original data set (Benoît, 2002; Han &
Kamber, 2001,Fayyed, Piatetsky-Shapiro, & Smyth, 1996).

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What is Data Mining?
Data mining refers to the process of finding interesting patterns in data that are
not explicitly part of the data (Witten & Frank, 2005, p. xxiii). The interesting patterns
can be used to tell us something new and to make predictions. The process of data
mining is composed of several steps including selecting data to analyze, preparing the
data, applying the data mining algorithms, and then interpreting and evaluating the
results. Sometimes the term data mining refers to the step in which the data mining
algorithms are applied. This has created a fair amount of confusion in the literature. But
more often the term is used to refer the entire process of finding and using interesting
patterns in data (Benoît, 2002).
The application of data mining techniques was first applied to databases. A better
term for this process is KDD (Knowledge Discovery in Databases). Benoît (2002) offers
this definition of KDD (which he refers to as data mining):
Data mining (DM) is a multistaged process of extracting previously
unanticipated knowledge from large databases, and applying the results to
decision making. Data mining tools detect patterns from the data and infer
associations and rules from them. The extracted information may then be
applied to prediction or classification models by identifying relations
within the data records or between databases. Those patterns and rules can
then guide decision making and forecast the effects of those decisions.
(p. 265)

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Today, data mining usually refers to the process broadly described by Benoît
(2002) but without the restriction to databases. It is a “multidisciplinary field drawing
work from areas including database technology, artificial intelligence, machine learning,
neural networks, statistics, pattern recognition, knowledge-based systems, knowledge
acquisition, information retrieval, high-performance computing and data visualization.
(Han & Kamber, 2001, p. xix).
Data mining techniques can be applied to a wide variety of data repositories
including databases, data warehouses, spatial data, multimedia data, Internet or Web-
based data and complex objects. A more appropriate term for describing the entire
process would be knowledge discovery, but unfortunately the term data mining is what
has caught on (Andrássoyá & Paralič, 1999).
Developmental History of Data Mining and Knowledge Discovery
The building blocks of today’s data mining techniques date back to the 1950s
when the work of mathematicians, logicians, and computer scientists combined to create
artificial intelligence (AI) and machine learning (Buchanan, 2006.).
In the 1960s, AI and statistics practitioners developed new algorithms such as
regression analysis, maximum likelihood estimates, neural networks, bias reduction, and
linear models of classification (Dunham, 2003, p. 13). The term “data mining” was
coined during this decade, but the term was pejoratively used to describe the practice of
wading through data and finding patterns that had no statistical significance (Fayyad, et
al., 1996, p. 40).

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Also in the 1960s, the field of information retrieval (IR) made its contribution in
the form of clustering techniques and similarity measures. At the time these techniques
were applied to text documents, but they would later be utilized when mining data in
databases and other large, distributed data sets (Dunham, 2003, p. 13). Database systems
focus on query and transaction processing of structured data, whereas information
retrieval is concerned with the organization and retrieval of information from a large
number of text-based documents (Han & Kamber, 2001, p. 428). By the end of the
1960s, information retrieval and database systems were developing in parallel.
In 1971, Gerard Salton published his groundbreaking work on the SMART
Information Retrieval System. This represented a new approach to information retrieval
which utilized the algebra-based vector space model (VSM). VSM models would prove
to be a key ingredient in the data mining toolkit (Dunham, 2003, p. 13).
Throughout the 1970s, 1980s, and 1990s, the confluence of disciplines (AI, IR,
statistics, and database systems) plus the availability of fast microcomputers opened up a
world of possibilities for retrieving and analyzing data. During this time new
programming languages were developed and new computing techniques were developed
including genetic algorithms, EM algorithms, K-Means clustering, and decision tree
algorithms (Dunham, 2003, p. 13).
By the start of the 1990s, the term Knowledge Discovery in Databases (KDD) had
been coined and the first KDD workshop held (Fayyad, Piatetsky-Shapiro, & Smyth,
1996, p. 40). The huge volume of data available created the need for new techniques for
handling massive quantities of information, much of which was located in huge
databases.

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The 1990s saw the development of database warehouses, a term used to describe a
large database (composed of a single schema), created from the consolidation of
operational and transactional database data. Along with the development of data
warehouses came online analytical processing (OLAP), decision support systems, data
scrubbing/staging (transformation), and association rule algorithms (Dunham, 2003, p.
13, 35-39; Han & Kamber, 2001, p. 3).
During the 1990s, data mining changed from being an interesting new technology
to becoming part of standard business practice. This occurred because the cost of
computer disk storage went down, processing power went up, and the benefits of data
mining became more apparent. Businesses began using “data mining to help manage all
phases of the customer life cycle, including acquiring new customers, increasing revenue
from existing customers, and retaining good customers” (Two Crows, 1999, p. 5).
Data mining is used by a wide variety of industries and sectors including retail,
medical, telecommunications, scientific, financial, pharmaceutical, marketing, Internet-
based companies, and the government (Fayyad, et al., 1996). In a May, 2004 report on
Federal data mining activities, the U.S. General Accounting Office (GAO, 2004) reported
there were 199 data mining operations underway or planned in various federal agencies
(p. 3), and this list doesn’t include the secret data mining activities such as MATRIX and
the NSA’s eavesdropping (Schneier, 2006).
Web mining is an area of much research and development activity. There are
many factors that drive this activity including online companies who wish to learn more
about their customers and potential customers, governmental agents tasked with locating
terrorists and optimizing services, and the user need for filtered information.

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Theoretical Principles
The underlying principle of data mining is that there are hidden but useful
patterns inside data and these patterns can be used to infer rules that allow for the
prediction of future results (GAO, 2004, p. 4).
Data mining as a discipline has developed in response to the human need to make
sense of the sea of data that engulfs us. Per Dunham (2003), data doubles each year and
yet the amount of useful information available to us is decreasing (p. xi). The goal of
data mining is to identify and make use of the “golden nuggets” (Han & Kamber, 2001,
p. 4) floating in the sea of data.
Prior to 1960 and the dawn of the computer age, a data analyst was an individual
with expert knowledge (domain expert) and training in statistics. His job was to cull
through the raw data and find patterns, make extrapolations, and locate interesting
information which he then conveyed via written reports, graphs and charts. But today,
the task is too complicated for a single expert (Fayyad, et al., 1996, p. 37). Information is
distributed across multiple platforms and stored in a wide variety of formats, some of
which are structured and some unstructured. Data repositories are often incomplete.
Sometimes the data is continuous and other times discrete. But always the amount of
data to be analyzed is enormous.
KDD involves searching large databases, but it distinguishes itself from database
querying in that it seeks implicit patterns in the data rather than simply extracting
selections from the database. Per Benoît (2002), the database query answers the question
“what company purchased over $100,000 worth of widgets last year?” (p. 270) whereas

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data mining answers the question “what company is likely to purchase over $100,000
worth of widgets next year and why?” (p. 270).
All forms of data mining (KDD included) operate on the principle that we can
learn something new from the data by applying certain algorithms to it to find patterns
and to create models which we then use to make predictions, or to find new data
relationships (Benoît, 2002; Fayyad, et al., 1996; Hearst, 2003).
Another important principle of data mining is the importance of presenting the
patterns in an understandable way. Recall that the final step in the KDD process is
presentation and interpretation. Once patterns have been identified, they must be
conveyed to the end user in a way that allows the user to act on them and to provide
feedback to the system. Pie charts, decision trees, data cubes, crosstabs, and concept
hierarchies are commonly used presentation tools that effectively convey the discovered
patterns to a wide variety of users (Han & Kamber, 2001, pp. 157-158).
Technological Elements of Data Mining
Because of the inconsistent use of terminology, data mining can both be called a
step in the knowledge discovery process or be generalized to refer to the larger process of
knowledge discovery.
Steps in Knowledge Discovery
Table 1 (Andrássoyá & Paralič, 1999, Section 2.2) compares the primary steps in
knowledge discovery as presented by different authors. The table helps us understand the
basic steps in the knowledge discovery process and where the specific application of data
mining fits into the larger picture.

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Simoudis Mannila Fayyad et al. Brachman & Anand
[12] [10] [5] [1]
understanding the learning the
task discovery
domain application domain
creating a target
data selection data discovery
dataset
data cleaning and
data cleaning
preprocessing
data reduction and
preparing the data
data transformation projection
set
choosing the model development
function
of data mining
choosing the data
discovering patterns mining algorithm(s)
Data mining data analysis
(data mining)
data mining
postprocessing of
result interpretation interpretation output generation
discovered patterns
putting the results using discovered
into use knowledge
Table 1: List of Knowledge Discovery Steps (Andrássoyá & Paralič,1999, Section 2.2)
Step 1: Task Discovery
The goals of the data mining operation must be well understood before the process
begins: The analyst must know what the problem to be solved is and what the questions
that need answers are. Typically, a subject specialist works with the data analyst to refine
the problem to be solved as part of the task discovery step (Benoît, 2002).

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Step 2: Data Discovery
In this stage, the analyst and the end user determine what data they need to
analyze in order to answer their questions, and then they explore the available data to see
if what they need is available (Benoît, 2002).
Step 3: Data Selection and Cleaning
Once data has been selected, it will need to be cleaned up: missing values must
be handled in a consistent way such as eliminating incomplete records, manually filling
them in, entering a constant for each missing value, or estimating a value. Other data
records may be complete but wrong (noisy). These noisy elements must be handled in a
consistent way (Benoît, 2002; Fayyad, et al., 1996).
Step 4: Data Transformation
Next, the data will be transformed into a form appropriate for mining. Per Weiss,
Indurkhya, Zhang & Damerau (2005), “data mining methods expect a highly structured
format for data, necessitating extensive data preparation. Either we have to transform the
original data, or the data are supplied in a highly structured format” (p. 1).
The process of data transformation might include smoothing (e.g. using bin means
to replace data errors), aggregation (e.g. viewing monthly data rather than daily),
generalization (e.g. defining people as young, middle-aged, or old instead of by their
exact age), normalization (scaling the data inside a fixed range), and attribute
construction (adding new attributes to the data set, Han & Kamber, 2001, p. 114).

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Step 5: Data Reduction
The data will probably need to be reduced in order to make the analysis process
manageable and cost-efficient. Data reduction techniques include data cube aggregation,
dimension reduction (irrelevant or redundant attributes are removed), data compression
(data is encoded to reduce the size, numerosity reduction (models or samples are used
instead of the actual data), and discretization and concept hierarchy generation (attributes
are replaced by some kind of higher level construct, Han & Kamber, 2001, pp. 116-117).
Step 6: Discovering Patterns (aka Data Mining)
In this stage, the data is iteratively run through the data mining algorithms (see
Data Mining Methods below) in an effort to find interesting and useful patterns or
relationships. Often, classification and clustering algorithms are used first so that
association rules can be applied (Benoît, 2002, p. 278).
Some rules yield patterns that are more interesting than others. This
“interestingness” is one of the measures used to determine the effectiveness of the
particular algorithm (Fayyad, et al.,1996; Freitas, 1999; Han & Kamber, 2001).
Fayyad, et al. (1996) states that interestingness is “usually taken as an overall
measure of pattern value, combining validity, novelty, usefulness, and simplicity” (p. 41).
A pattern can be considered knowledge if it exceeds an interestingness threshold. That
threshold is defined by the user, is domain specific, and “is determined by whatever
functions and thresholds the user chooses” (p. 41).

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Step 7: Result Interpretation and Visualization
It is important that the output from the data mining step can be “readily absorbed
and accepted by the people who will use the results” (Benoît, p. 272). Tools from
computer graphics and graphics design are used to present and visualize the mined
output.
Step 8: Putting the Knowledge to Use
Finally, the end user must make use of the output. In addition to solving the
original problem, the new knowledge can also be incorporated into new models, and the
entire knowledge or data mining cycle can begin again.
Data Mining Methods
Common data mining methods include classification, regression, clustering,
summarization, dependency modeling, and change and deviation detection. (Fayyad, et
al., 1996, pp. 44-45)
Classification
Classification is composed of two steps: supervised learning of a training set of
data to create a model, and then classifying the data according to the model. Some well-
known classification algorithms include Bayesian Classification (based on Bayes
Theorem), decision trees, neural networks and backpropagation (based on neural
networks), k-nearest neighbor classifers (based on learning by analogy), and genetic
algorithms. (Benoît, 2002; Dunham, 2003).

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Decision trees are a popular top-down approach to classification that divides the
data into leaf and node divisions until the entire set has been analyzed. Neural networks
are nonlinear predictive tools that learn from a prepared data set and are then applied to
new, larger sets. Genetic algorithms are like neural networks but incorporate natural
selection and mutation. Nearest neighbor utilizes a training set of data to measure the
similarity of a group and then use the resultant information to analyze the test data.
(Benoît, 2002, pp. 279-280)
Regression
Regression analysis is used to make predictions based on existing data by
applying formulas. Using linear or logistic regression techniques from statistics, a
function is learned from the existing data. The new data is then mapped to the function in
order to make predictions. (Dunham, 2003, p. 6) Regression trees, decision trees with
averaged values at the leaves, are a common regression technique. (Witten & Frank,
2005, p. 76)
Clustering
Clustering involves identifying a finite set of categories (clusters) to describe the
data. The clusters can be mutually exclusive, hierarchical or overlapping. (Fayyad, et al.,
1996, p. 44). Each member of a cluster should be very similar to other members in its
cluster and dissimilar to other clusters. Techniques for creating clusters include
partitioning (often using the k-means algorithm) and hierarchical methods (which group
objects into a tree of clusters), as well as grid, model, and density-based methods. (Han
& Kamber, 2001, p. 346-348)

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Outlier analysis is a form of cluster analysis that focuses on the items that don’t
fit neatly into other clusters (Han & Kamber, 2001). Sometimes these objects represent
errors in the data, and other times they represent the most interesting pattern of all.
Freitas (1999) focuses on outliers in his discussion of attribute surprisingness and
suggests that another criterion for interestingness measures should be surprisingness.
Summarization
Summarization maps data into subsets and then applies a compact description for
that subset. Also called characterization or generalization, it derives summary data from
the data or extracts actual portions of the data which “succinctly characterize the
contents” (Dunham, 2003, p. 8).
Dependency Modeling (Association Rule Mining)
Dependency or Association Rule Mining involves searching for interesting
relationships between items in a data set. Market basket analysis is a good example of
this model. An example of an association rule is “customers who buy computers tend to
also buy financial software” (Han & Kamber, 2001, pp. 226-117). Since association
rules are not always interesting or useful, constraints are applied which specify the type
of knowledge to be mined such as specific dates of interest, thresholds on statistical
measures (rule interestingness, support, confidence), or other rules applied by end users
(Han & Kamber, 2001, pp. 262).
Change and Deviation Detection
Also called sequential analysis and sequence discovery (Dunham, 203, p. 9),
change and deviation detection focuses on discovering the most significant changes in

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data. This involves establishing normative values and then evaluating new data against
the baseline (Fayyad, et al., 1996, p. 45). Relationships based on time are discovered in
the data.
The above methods form the basis for most data mining activities. Many
variations on the basic approaches described above can be found in the literature
including algorithms specifically modified to apply to spatial data, temporal data mining,
multi-dimensional databases, text databases and the Web (Dunham, 2003; Han &
Kamber, 2001).
Related Disciplines: Information Retrieval and Text Mining
Two disciplines closely related to data mining are information retrieval and text
mining. The relationship between information retrieval and data mining techniques has
been complementary. Text mining, however, represents a new discipline arising from the
combination of information retrieval and data mining.
Information Retrieval (IR)
Many of the techniques used in data mining come from Information Retrieval
(IR), but data mining goes beyond information retrieval. IR is concerned with the
process of searching and retrieving information that exists in text-based collections
(Dunham, 2003, p. 26). Data mining, on the other hand, is not concerned with retrieving
data that exists in the repository. Instead, data mining is concerned with patterns that can
be found that will tell us something new – something that isn’t explicitly in the data (Han
& Kamber, 2001).

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IR techniques are applied to text-based collections (Baeza-Yates & Ribeiro-Neto,
1999). Data mining techniques can be applied to text documents as well as databases
(KDD), Web based content and metadata, and complex data such as GIS data and
temporal data.
In terms of evaluating effectiveness, IR and data mining system markedly differ.
Per Dunham (2003, p. 26), the effectiveness of an IR system is based on precision and
recall and can be represented by the following formulas:
Precision = Relevant and Retrieved
Retrieved
Recall = Relevant and Retrieved
Relevant
The effectiveness of any knowledge discovery system is whether or not any useful
or interesting information (knowledge) has been discovered. Usefulness and
interestingness measures are much more subjective than IR measures (precision and
recall).
IR Contributions to Data Mining
Many of the techniques developed in IR have been incorporated into data mining
methods including Vector Space Models, Term Discrimination Values, Inverse
Document Frequency, Term Frequency-Inverse Document Frequency, and Latent
Semantic Indexing.
Vector Space Models, or vector space information retrieval systems, represent
documents as vectors in a vector space (Howland & Park, 2003, p. 3; Kobayashi &
Aono, 2003, p. 105). Term Discrimination Value posits that a good discriminating term

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is one that, when added to the vector space, increases the distances between documents
(vectors). Terms that appear in 1%-10% of documents tend to be good discriminators
(Senellart & Blondel, 2003, p. 28). Inverse Document Frequency (IDF) is used to
measure similarity. IDF is used in data mining methods including clustering and
classification (Dunham, 2003, pp. 26-27). Term Frequency-Inverse Document
Frequency (TF-IDF) is an IR algorithm based on the idea that terms that appear often in a
document and do not appear in many documents are more important and should be
weighted accordingly (Senellart & Blondel, 2003, p. 28). Latent Semantic Indexing (LSI)
is a dimensional reduction process based on Singular Value Decomposition (SVD). It
can be used to reduce noise in the database and help overcome synonymy and polysemy
problems (Kobayashi & Aono, 2003, p. 107).
Data Mining Contributions to IR
Although IR cannot utilize all the tools developed for data mining because IR is
generally limited to unstructured documents, it has nonetheless benefited from advances
in data mining. Han and Kamber (2001) describe Document Classification Analysis
which involves developing models which are then applied to other documents to
automatically classify documents. The process includes creating keywords and terms
using standard information retrieval techniques such as TF-IDF and then applying
association techniques from data mining disciplines to build concept hierarchies and
classes of documents which can be used to automatically classify subsequent documents
(p. 434).
The data mining idea of creating a model instead of directly searching the original
data can be applied to IR. Kobayashi & Aono (2003) describe using Principle

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Component Analysis (PCA) and Covariance Matrix Analysis (COV) to map an IR
problem to a “subspace spanned by a subset of the principal components” (p. 108).
Text Mining
Text mining (TM) is related to information retrieval insofar as it is limited to text.
Yet it is related to data mining in that it goes beyond search and retrieval. Witten and
Frank (2005) explain that the information to be extracted in text mining is not hidden;
however, it is unknown because in its text form it is not amenable to automatic
processing. Some of the methods used in text mining are essentially the same methods
used in data mining. However, one of the first steps in text mining is to convert text
documents to numerical representations which then allows for the use of standard data
mining methods (Weiss, Indurkhya, Zhang & Damerau, 2005).
Per Weiss, et al. (2005), “one of the main themes supporting text mining is the
transformation of text into numerical data, so although the initial presentation is different,
at some intermediate stage, the data move into a classical data-mining encoding. The
unstructured data becomes structured” (pp. 3-4).
Weiss, et al (2005) use the spreadsheet analogy as the classical data mining model
for structured data. Each cell contains a numerical value that is one of two types:
ordered numerical or categorical. Income and cost are examples of ordered numerical
attributes. Categorical attributes are codes or true or false. In text mining, the idea is to
convert the text presented as a document to values presented in one row of a spreadsheet
where each row represents a document and the columns contain words found in one or
more documents. The values inside the spreadsheet can then be defined (categorically) as

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present (this word is in this document) or absent (this word is not in this document). The
spreadsheet represents the entire set of documents or corpus.
The collection of unique words found in the entire document collection represents
the dictionary and will likely be a very large set. However, many of the cells in the
spreadsheet will be empty (not present). An empty cell in a data mining operation might
pose a problem, as it would be interpreted as an incomplete record. However, in text
mining, this sparseness of data works to reduce the processing requirements because only
cells containing information need to be analyzed. The result is that the size of the
spreadsheet is enormous but it is mostly empty. This “allows text mining programs to
operate in what would be considered huge dimensions for regular data-mining
applications” (Weiss, et al., 2005, p. 5).
Per Weiss, et al. (2005), the process of getting the text ready for text mining is
very much like the knowledge discovery steps described earlier. In text mining, the text
is usually converted first to XML format for consistency. It is then converted to a series
of tokens (sometimes punctuation is interpreted as a token, sometimes as a delimiter).
Then, some form of stemming is applied to the tokens to create the standardized
dictionary. Familiar IR/data mining processes such as TF-IDF can be applied to assign
different weights to the tokens. Once this has been done, classification and clustering
algorithms are applied.
Depending on the goal of the text mining operation, it may or may not be
important to incorporate linguistic processing in the text mining process. Examples of
linguistic processing include marking certain types of words (part-of-speech tagging),

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clarifying the meaning of words (disambiguation) and parsing sentences. Per Benoît
(2002),
Text mining brings researchers closer to computational linguistics, as it
tends to be highly focused on natural language elements in texts (Knight,
1999). This means TM applications (Church & Rau, 1995) discover
knowledge through automatic content summarization (Kan & McKeown,
1999), content searching, document categorization, and lexical,
grammatical, semantic, and linguistic analysis (Mattison, 1999). (p. 291)
Conclusion
Data mining is a synonym for knowledge discovery. Data mining also refers to a
specific step in the knowledge discovery process, a process that focuses on the
application of specific algorithms used to identify interesting patterns in the data
repository. These patterns are then conveyed to an end user who converts these patterns
into useful knowledge and makes use of that knowledge.
Data mining has evolved out of the need to make sense of huge quantities of
information. Usama M. Fayyad says that stored data is doubling every nine months and
the “demand for data mining and reduction tools increase exponentially (Fayyad,
Piatetsky-Shapiro, & Uthurusamy, 2003, p. 192).” In 2006, $6 billion in text and data
mining activities are anticipated (Zanasi, Brebbia, & Ebecken, 2005).
The U.S. government is involved in many data mining initiatives aimed at
improving services, detecting fraud and waste, and detecting terrorist activities. One
such activity, the work of Able Danger, had identified one of the men who would, one
year later, participate in the 9/11 attacks (Waterman, 2005). This fact emphasizes the

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importance of the final step of the knowledge discovery process: putting the knowledge
to use.
The U.S. government’s data mining activities have helped stir concerns about data
mining and their impact on privacy (Boyd, 2006). Privacy preserving data mining has
only recently caught the attention of researchers (Verykios, Bertino, Fovino, Provenza,
Saygin & Theodoridis, 2004).
There is much work to done in the area of knowledge discovery and data mining,
and its future depends on developing tools and techniques that yield useful knowledge
without causing undue threats to individuals’ privacy.